{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from utils.matplot import histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 泊松"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import poisson\n",
    "x = np.arange(0, 100, 0.5) # creating a numpy array for x-axis\n",
    "y = poisson.pmf(x, mu=40, loc=10) # # poisson distribution data for y-axis\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# beta pert分布\n",
    "beta_pert = np.random.beta(4.5000, 258.5000, 100000)\n",
    "beta_pert = pd.DataFrame(beta_pert, columns=['d'])\n",
    "histogram(beta_pert, *['d'], num_bins=50, figscale=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 正态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 正态分布\n",
    "a=np.random.normal(size=10000)\n",
    "df = pd.DataFrame(a, columns=['d'])\n",
    "histogram(df, *['d'], **{})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1对应的概率密度函数 3.598144833548283\n",
      "0.1对应的累积概率函数 0.16725271744552878\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "rv = norm(loc=0, scale=1)\n",
    "rv = norm(loc=0.167165805834457, scale=0.06959620125339538)\n",
    "print(f\"0.1对应的概率密度函数 {rv.pdf(0.1)}\")\n",
    "print(f\"0.1对应的累积概率函数 {rv.cdf(0.1)}\")\n",
    "x = np.linspace(-3, 3, 100)  # 生成一些x值\n",
    "pdf = rv.pdf(x)  # 计算对应x值的概率密度\n",
    "cdf = rv.cdf(x)  # 计算对应x值的概率密度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/norm.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "概率密度函数 3.9470740790642966\n",
      "累积概率函数 0.19568296915377603\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "# rv = norm(loc=0, scale=1)\n",
    "rv = norm(loc=0.16, scale=0.07)\n",
    "print(f\"概率密度函数 {rv.pdf(0.1)}\")\n",
    "print(f\"累积概率函数 {rv.cdf(0.1)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3989422804014327"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/((np.pi * 2 )**0.5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mlflow-dev-env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
